Topic Trends Tracker: Google Trends Alternative, Real Numbers

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

The annotations already establish read-only, open-world, non-idempotent behavior, and the description adds important operational details: it runs the named Apify Actor on the user's own account, charges $0.002 per topic, does not charge per result for runs that find nothing, and supports a hard spending cap. It does not explain data freshness beyond the schema's 'ending yesterday' note or the exact response shape, but the cost and execution behavior are unusually well disclosed.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Is the description appropriately sized, front-loaded, and free of redundancy?

The description is front-loaded with the core use case and then adds pricing and execution details in a compact second paragraph. The Apify Actor link and price information are relevant rather than filler. It could be slightly tighter, but there is little wasted wording.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a 10-parameter tool with an optional schema and no output schema, the description covers the main outputs (daily and monthly views, 30-day and year-over-year change, peaks, spike days, a 0–100 index, Excel and CSV), cost model, and execution environment. It does not describe pagination or precise response structure, but the schema already documents inputs thoroughly and the annotations cover safety semantics.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema description coverage is 100%, so every parameter is already documented in the input schema. The description does not add parameter-level meaning beyond mentioning topics, Excel/CSV output, and the spending cap. This meets the baseline for a fully documented schema but does not enrich parameter interpretation.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Does the description clearly state what the tool does and how it differs from similar tools?

The description opens with a specific verb-and-resource statement: compare public interest in topics, brands, products, or people over time using Wikipedia pageview counts. It clearly distinguishes the tool from Google Trends by naming the underlying data source and listing the metrics it returns. The purpose is immediately understandable without opening the schema.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Does the description explain when to use this tool, when not to, or what alternatives exist?

It gives a clear use case: comparing public interest in topics over time with real view counts. It does not name when-not-to-use conditions or explicit alternatives, but the sibling tools are largely unrelated research utilities, so there is little ambiguity to resolve. The usage context is adequate, though not fully prescriptive.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.